The following sample essay on “Benefits of Relationship Banking : Evidence from Consumer Credit Markets”: the purpose of the study is examines the benefits of relationship banking to banks, in the context of consumer credit markets.
The problem with this study is that the account exhibits a lower probability of default, and the loss has a higher utilization rate than the non-relational account. The researcher did the study to investigate the significant potential benefits of relationship banking to banks in the retail credit market.
The scope of this study examines the implications of bank relationships for key aspects of credit card behavior, such as default, attrition and utilization rates.
The underlying theory of the study is the bank is serving as a relationship lender. When a bank provides more services to its customers, it build stronger relationship with customer and gets more personal information about him or her. Such relationship may benefit bank and their customer. Relationship banking also can reduce the cost of collecting information on a variety of products.
Sampling method is the method that selecting a number of individuals for a study in such a way that the individuals represent the larger group form which they were selected. In this study, the author has used a sample of 100 Canadian small- business borrowers to investigate the benefits of particular relationship information in monitoring the risk of corporate loans. Furthermore, the researcher has used the Survey of Consumer Finance (SCF) to conclude the relationship lending for the consumer loans.
For this study, the author uses the secondary data to collect the information. Secondary data refers to data which is collected by someone who is someone other than the user. The author do this research by using the secondary data to find all of the information and data about the benefit of relationship banking, evidence from consumer credit markets. All of the resources has been recorded in the references by the author.
The data was collected by content analysis and using internet. This research use unique proprietary panel data sets and related relationship information from credit card accounts of large national financial institutions. The data set contains a representative sample of approximately 100,000 accounts opened on October 2001 and monthly for the next 24 months. The data set includes key information that banks use to mange their credit card accounts. The data set contains the primary billing information listed on each accounts monthly statement such as total payments, expenses, balances and debts, as well credit lines and APRs. Moreover, the research also using the Survey of Consumer Finance (SCF) to conclude the relationship lending for the consumer loans. Furthermore, the author also use questionnaire method to investigate the benefits of particular relationship information in monitoring the risk of corporate loans.
The researcher use the quantitative method and the regression analysis to analysis the data. In this research, the author has provides a summary statistics for the key variables used, average over two years during the sample period. The table has been distinguished into three part which is relationship accounts, non-relationship accounts, and no other relationships. The holders in the relationship account have higher average income and higher wealth Furthermore, their accounts have less debt and have higher internal and external credit scores. Overall, relational accounts typically appear to be less risky than the non-relationship accounts based on information in public and private accounts.
In this research, the authors have discovered that the survival curves increases monotonically with the number of relationships. For example, an account with only one other relationship have the probability of not defaulting within 48 months is about 96% compare with the account which got six or more relationships, that probability significantly has rises to about 99%. Moreover, the researcher have found out many credit variables is very important although their marginal effects are much smaller. The probability of default increases significantly with the amount of debt in the credit card account and the total number of credit cards held by account holders no matter bank cards or non-bank cards and the balance on those cards. A larger credit limit or a lower APR in the account also associated with the lower default probability. When it come to macroeconomic, the author stated that demographic variables, unfavorable local economic conditions are often associated with more default. Even come to the account state and monthly dummies, the higher local unemployment rates and lower house price growth will lead to a significant increase in default rates. Furthermore, the researcher has found out that the various results regarding checking relationships mean that dynamic information from checking account is especially useful for continuously monitoring loans. Changes in the behavior of a checking account can provide indirect information about shocks and other factors that banks cannot directly observe.
In the relationship banking and credit card default and attrition, the relationship account has a lower probability of default under all measures for relationships R1-R7. Similar relationship measure has been considered in previous literature on corporate lending. In an attempt to distinguish between specific dynamic concepts of relationship interests, subsequent specifications more explicitly consider dynamic measures of relationship information. R9 increases over time in relationship balances are associated with declines in default risk, ceteris paribus. The volatile relationship balances of R10 are associated with higher default risk. The coefficient of R11, R12, R13 are significantly positive.
In the relationship banking and credit card utilization, the coefficient of the relationship measure R1 and R7 are significantly positive. With the measure R2, utilization is significantly and monotonously increasing with the number of relationships. Under measure R3 and R4, utilization increases with each type of relationship, especially inspection and brokerage relationships. At R5, utilization also increases with the length of each relationship. Under R6, the interaction of the relationship indicator (R1) with the proximity indicator results in a significant positive coefficient. With R9, changes in relationship balances often have a positive impact. At R10 and R11, the higher volatility of the balance is associated with lower utilization. Under R12, the low balance indicator is not important given the check balance level (R7). However, according to R13, the transfer of the balance to other accounts is associated with a significant increase in credit card utilization.
The literature reviews was sufficient because in the early research proves that the existence of bank relationship increases the value of the company. Subsequent research attempts to measure the impact of relationships on corporate credit supply. These studies emphasize different aspects of relationship, such as their breadth, depth, length and proximity. It is not robust regarding the sampling, methodology and result because the researcher has found the information about customer collateral, especially their inventory and accounts receivable, which may not be available to banks outside the relationship and are useful for loan monitoring. In addition, change in the transaction account can provide information about changes to this collateral.
The study is same compared with the previous study. In this research, an increase in the standard deviation of the unemployment rate which decreased house price growth rate is equivalent to a 3% increase in default probability which an increase of 8%. Although the result are not statistically significant but the higher incomes and wealth are associated with less defaults. These results generally consistent with previous studies which is Gross and Souleles studies, 2002.
Yes. This results or findings has been totally affect my study. Though this study, I have know the potential benefits of relationship banking to retail banks. The result has been show me that even if the traditional sources of bank information such as public information and private, within-account information and other variable are controlled, the bank account holder of other relationships often have higher utilization rates, but default and attrition rates. In particular, dynamic information about changes in other relationship behaviors of account holders can help predict changes in credit card accounts over time. From this study, I have found out that relationships can help banks better monitor their loans in the various potential benefits of relational bank. These results mean that relationship information is valuable in predictive terms, but how banks should use this information requires additional considerations.
Furthermore, I found out that the optimal use of information and optimal contract design from the perspective of banks and society is very important but difficult to solve the problem. In order to solve these problem, the author has give some advise such as banks need to consider how the consumers and their competitors respond to the use of information. Secondly, government policies should limit the certain uses of information such as cross-account information. Thirdly, a comprehensive analysis of such policies should also consider the potential efficiency gains of excluding predictable information in order to considering the benefits of such restrictions.